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Object-Based Classification of Sentinel-2 Imagery Using Compact Texture Unit Descriptors Through Google Earth Engine

Auteurs: » Djerriri Khelifa
» Abdelmounaime Safia
» ADJOUDJ Reda
Type : Conférence Internationale
Nom de la conférence : 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS)
Lieu : Pays:
Lien : »
Publié le : 09-03-2020

This study investigates the possibilities of improving the classification of high spatial resolution images by using object-based approach, superpixel segmentation and compact texture unit descriptors. The proposed approach was implemented on Google Earth Engine (GEE) which provides a fast and easy-to-use platform with its freely available datasets and geospatial analysis tools for applications such multi-class classification. In this work, Multispectral Instrument (MSI) images of Sentinel-2 were utilized to classify main land-cover and land-use types. The obtained results were validated using the Corine land cover inventory.

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